Decision Guide

The first Digital FTE decides whether there is a second. This decision guide shows how to pick a workflow that will earn its place, what has to be in place before you start, and the sequence that gets it to accountable operation.

Computer-use agents are powerful and expensive, and they are the wrong first choice for most integrations. This decision guide gives the order of options, the task properties that make screen control worthwhile, and the cases to avoid.

Leaderboards rank models on public tasks; your agents run on private data with specific quality bars. This decision guide gives an evaluation-driven method for choosing models per workload, and the criteria beyond benchmark scores that decide enterprise fit.

Reasoning models think longer and cost more, and for many agent steps that is wasted. This decision guide gives the task properties that justify them, the cascade pattern that uses them only when needed, and the evidence to require before paying the premium.

Open-source LLMs win on cost at volume, version control, residency, and customization for bounded tasks, while hosted frontier models lead on broad capability. This guide covers the cases for each, the evaluation that decides, the operational and licensing realities, and the hybrid pattern behind one gateway.

Small language models match large ones on many bounded, high-volume tasks at a fraction of the cost and latency. This guide covers which tasks fit, how to prove the fit on your golden dataset, routing patterns that send hard requests to larger models, and the mistakes that turn savings into quality regressions.

Bringing AI development in-house is right when systems are core and evolving, volume justifies full-time roles, partners have transferred the platform and practices, and you can hire the skills. This guide covers the signals, the transition sequence, what to keep outsourced, and the mistakes that lose momentum.

Fine-tuning is the right tool for a narrow set of problems and the wrong first move for most. This guide covers when it beats prompting and retrieval, what it costs in data, compute, and maintenance, the evaluation needed to prove the gain, and the decision sequence to follow before committing.

AI agencies fit defined deliverables with fixed scope where you do not need the capability afterward. This guide covers when an agency is the right call, when embedded engineers or in-house teams fit better, how to keep ownership of code, data, and prompts either way, and the warning signs in agency proposals.

Stopping an AI project is a decision most organizations cannot make because nobody defined what stopping would look like. This guide covers the kill criteria to set at approval, the signals that a project should stop, how to decide at checkpoints without politics, and what to salvage for the next initiative.

Outsourcing AI development fits when speed, scarce skills, platform establishment, or flexible capacity matter more than immediate in-house ownership. This guide covers the conditions, which work to outsource and which to keep, how to outsource delivery without outsourcing ownership, and the warning signs in engagements.

Self-hosting LLMs is justified by residency, control, sustained volume, or latency, and expensive in GPUs and operations otherwise. This guide covers each case, how to model the real cost against hosted pricing, the operational requirements, and the hybrid pattern most organizations end up with.

Knowledge graphs answer relationship questions that passage retrieval cannot, at a build and maintenance cost most retrieval systems do not need. This guide covers the question types that justify a graph, how to test whether your baseline fails on them, the costs, simpler alternatives, and a decision method.

A dedicated vector database is justified by scale, filtered low-latency queries under load, heavy index updates, or multi-tenant isolation. For moderate scale, a vector extension on the database you already run is usually the right start. This guide covers the thresholds, trade-offs, hybrid search needs, and a decision method.

Multi-agent systems are justified when a task needs distinct skills, tools, or data access, parallel subtasks, or separated permissions, and they add coordination cost, complexity, and new failure modes. This guide covers the justifications, the costs, single-agent alternatives, and a decision method based on evaluation.

Prioritizing AI use cases means one inventory, one scoring rubric, sequencing for foundations and quick evidence, limited work in flight, and rescoring at checkpoints. This guide walks through the method step by step, with the questions to ask at each stage and the mistakes that produce pilot sprawl.

Boards need to govern AI, not admire it. This guide covers what directors actually need, a one-page reporting format backed by evidence, the portfolio and risk metrics to include, how to present decisions needed, and the reporting mistakes that cost technology leaders credibility.

AI discovery is the short phase before build that decides whether and how to proceed. This guide covers measuring the baseline, defining correct with domain experts, testing feasibility on a first golden dataset, verifying data and integration, modeling cost, and producing a specification with a proceed, narrow, or stop decision.

An AI steering committee directs the portfolio: it approves and sequences initiatives, releases staged funding on evidence, resolves cross-functional blockers, and escalates to governance. This guide covers charter, membership, cadence, decision rules, the evidence to review, and how to keep it from decaying into status updates.

AI KPIs work when they cover five categories, tie to baselines, change targets by stage, and come from the same dashboards every time. This guide defines the categories, gives example metrics for each, explains how targets evolve from pilot to scale, and names the vanity metrics that mislead leadership.

AI pilots drift into production without a decision when the agreement never defined success. This guide covers bounded scope, acceptance criteria as measurements, data and IP terms, pricing, a decision date, and conversion terms, so the pilot proves something and ends in proceed, narrow, or stop.

Works as expected is not an acceptance criterion for a probabilistic system. This guide covers writing criteria as measurable thresholds by category on named datasets, safety and groundedness tests, latency and cost limits, failure behavior, operability, and documentation, each with an evaluation method and an owner, with worked examples.

An AI specification defines what the system is for and how it will be judged before build. This guide provides the sections, from workflow and scope through correctness, acceptance criteria, autonomy and gates, failure behavior, data, integration, budgets, and risks, with guidance on writing each and the mistakes that produce demos instead of systems.

Choosing a web framework means matching rendering strategies to content and interactivity, ensuring indexable content reaches search engines and AI crawlers, and verifying support for streaming AI interfaces. This guide compares the main options, provides a decision method, and covers the mistakes that lead to migrations.